Netnography as a marketing research tool in the fashion industry in Southeast Europe

The concept of online communities

Online communities, retroactively referred to as virtual communities, were first recognized by Rheingold (1993). Since then, they have grabbed the attention of numerous academics, who consider them to be a dominant research subject (Kozinets, 2002; Malinen, 2015). Scholars view online communities as virtual social gatherings between individuals who have common interests, and intentionally share information and knowledge by interacting with each other (Liou, Chih, Hsu, & Huang, 2016). In these communities, members become producers of content, and hence, the online communities become directly dependent on the users’ content generation (Malinen, 2015).

Since their emergence, online communities have attracted the attention of a large number of Internet users (Wang et al., 2013). This is a result of the communities’ ability to create, discuss, and evaluate information. Consequently, the members of these communities express their attitudes, feelings, experiences, and search for advice/support from other members. They voluntarily spend time within these communities (Dessart, Veloutsou, & Morgan-Thomas, 2015) and share information about various topics.

As marketers recognized the importance of online communities, they themselves started to create brand communities, and encouraged consumers to participate (e.g., Ford Bronco; Malinen, 2015). This led to an exclusively passive consumption of information by consumers. The emergence of Web 2.0, however, has reshaped the way online communities are organized, as consumers create their online brand as well as anti-brand communities. Significantly for marketers, consumers form online communities devoted to specific topics of interest (Kamboj & Rahman, 2017).

The variety of topics discussed in online communities goes from health to autos to fashion (Zha, Zhang, Yan, & Xiao, 2014). For instance, Trendtation is an online community, which gathers fashion enthusiasts who love discussing the latest fashion trends and sharing their style with each other. Besides reading blogs of the main community influencers, Trendtation provides for its members the opportunity to buy and sell used fashion products. The existence of such secondary fashion markets demonstrates the importance of online communities.

Among others, online communities offer members “congenial information environments”; members post their queries freely and receive responses immediately from other members (Dessart et al., 2015). Zha et al. (2014) and argue that this information exchange is the main reason that Internet users join them. Apart from finding information, they participate within such communities to gain companionship, a sense of belonging, a status, and acquire social resources (Wang et al., 2013).

Virtual communities, like traditional face-to-face communities, establish and maintain social norms, codes, and conventions, in order to adjust interactions between members. They have “1. a shared consciousness; 2. rituals and traditions; and 3. a sense of moral responsibility” (Muniz & O’guinn, 2001, p. 412). Thus, these communities reflect subcultures of consumption and exist for both social reasons and shared brand experiences. These have been recognized by marketers, who see them as crucial for gathering and analyzing consumers’ perceptions (Mahajan, 2015; Prior & Miller, 2012).

Methods for analyzing consumer insights in online communities

According to Malinen (2015), online communities are primarily researched using quantitative methods—64% (e.g., survey research, quantitative content analysis, daily traffic analysis, website analytics); these methods provide a limited understanding of communities’ underlying motivations and interactions because they focus on the structured content (behaviors) rather than the context (the underlying motivations or the “why” of the behaviors). Therefore, a number of scholars (Parrot et al. 2015; Quinton & Wilson, 2016) and commercial market researchers suggest using qualitative methods in addition to quantitative methods, since they offer a detailed understanding of consumer motivations, perceptions, opinions, and experiences.

The majority of qualitative methods resemble traditional offline methods; nevertheless, they have been modified to adapt to the new factor—the Internet (Mahajan, 2015). Subsequently, scholars have recommended new Internet-based qualitative methods, and according to Valck et al. (2009) and La Rocca et al. (2014), the most prominent of them is netnography.

The concept of netnography

Netnography is a relatively new Internet-based qualitative research method, which has been embraced by researchers worldwide (La Rocca et al., 2014; Prior & Miller, 2012). Netnography is used to study emerging online communities and consumer motivations and behavior, and is conducted entirely online (Bartl, Kannan, & Stockinger, 2016). Kozinets is considered to be the original developer of the concept of netnography; hence, his work is paramount within the research literature. Kozinets (2002) recognizes the presence of anthropological ethnography elements in netnography, but he emphasizes that “this combination of more rigorous online guidelines combined with an innate flexibility is novel” (p. 64). Apart from Kozinets, Hine (2000) was among the first advocates of this method. She, on the other hand, views netnography as a form of ethnography, adapted to the Internet. Markham (2005), however, does not consider it as an Internet adaptation of ethnography; she considers this approach to be groundbreaking because it brought into focus a new area of social life—online communities.

During the past 20 years, this research method has gained momentum. This is a result of the rapid growth of Internet usage by consumers, who are increasingly turning to online communities to search for information to make purchasing decisions (Kozinets, 2012; Wang et al., 2013). Consequently, researchers’ interest in understanding the impacts that online communities’ membership has on consumer behavior has increased. Recently, scholars have noticed the potential of these communities, and they have been content to use netnography as a method to explore consumer behavior online (Xun & Reynolds, 2010). Also, commercial market researchers consider it to be “one of the most exciting new ways of accessing consumer insights” (Puri, 2007, p. 387). Nevertheless, we believe the full potential of this research method is still not recognized (Costello, McDermott, & Wallace, 2017; Wang et al., 2013).

Netnography’s strengths and weaknesses

Netnography is considered to be more economically viable than methods that require face-to-face contact, as it does not require physical travel (Valck et al., 2009; Xun & Reynolds, 2010). The primary advantage of this method to marketers, however, is the naturally occurring characteristic—netnography is unobtrusive and it does not interfere with the interactions between members (Kozinets, 2012). In effect, it offers the market researchers a natural environment to observe consumers, particularly a “real social context” (Puri, 2007), which creates numerous opportunities for analysis.

Using netnography, marketers are potentially able to collect information about the underlying reasons behind consumers’ purchase of products/brands, which could be then used to encourage consumer loyalty (Healy & McDonagh, 2013). For instance, Gamboa and Gonçalves (2014) found that Facebook assists in enhancing brand loyalty among Zara fans. In addition, this approach offers marketers access to authentic consumer responses (Costello et al., 2017), thus enabling them to observe a considerable amount of data in various contents, which improves the breadth and depth of research (Prior & Miller, 2012).

This approach is perceived as being effective for developing new products and strategies. It assists marketers in identifying the latest market trends, as well as it helps in developing new innovative concepts (Costello et al., 2017). For example, netnography provided Nokia with insights on the appearance of their phones, emphasizing that they were not compatible with their superior technology as Nokia’s competitors; thus, it made Nokia update with real-time trends (Puri, 2007). Furthermore, netnography has a “voyeuristic quality” (Kozinets, 2012). It is considered to be a suitable method when the researcher deals with sensitive topics (Langer & Beckman (2005) used it to study cosmetic surgery, whereas Gurrieri & Cherrier (2013) used it for fat activism).

On the other hand, netnography is still only partially developed as a method and has a number of shortcomings (Clemente-Ricolfe, 2017). Netnography faces three main issues: “community scope, data validity and data reliability” (Prior & Miller, 2012, p. 508). Prior & Miller (2012) point out the fact that such methodology focuses solely on the online interaction within communities. They believe that this offers a “discrete research context”; however, it limits research’s potential because it ignores the actual scope of the community being investigated, as its members might communicate offline as well. Members, occasionally, might have shared commitment for consumption activities, such as participating at a fashion designer’s event. This, nevertheless, differs significantly among communities (Canniford, 2011).

Netnographers observe that it is difficult to determine the quality of data because of the ability to establish a false identity. Because of this issue, Prior & Miller (2012) suggest putting greater emphasis on external validity, and they recommend the triangulation of data; for instance, by conducting face-to-face interviews with members of online communities.

Netnography’s procedures

Costello et al. (2017) and Pollok, Lüttgens, and Piller (2014) recognize numerous misconceptions about netnography and its procedures. They have noticed that a number of researchers consider any analysis of interaction in online communities to be a netnographic approach. Only a small number of researchers describe, explore, and evaluate the procedures used for conducting netnography. According to Costello et al. (2017), even the self-identified netnographers have a tendency to narrow the scope of its procedures—they select easy-to-collect data and easier analyzing techniques. This has created a need for future studies to report on and discuss netnography’s procedures (Pollok et al., 2014).

Despite the aforementioned misconceptions, netnography has already established procedures for conducting research. Puri (2007) suggests considering the following issues when conducting it: (1) searching for the best “texts” to study in chat rooms, social networks, or blogs and (2) identifying the best analysis tools available.

Kozinets (2002), however, suggests netnographers use a particular structure: (1) entrée (examining the potential online communities, selecting the appropriate one and getting familiar with it), (2) data collection and analysis (observing community members’ interactions and gathering the relevant data, which can be analyzed manually or through software tools), (3) providing trustworthy interpretation (triangulating data through interviews or focus groups), (4) research ethics (getting consent from community’s members), and (5) member checks (reporting the research to participants) (Kozinets, 2002). Kozinets’ approach to netnography appears to be a clearer step-by-step guide to practitioners, as having such a structured approach provides integrity to the research process and allows for the comparison of results.

The divergence of netnography’s procedures

The procedure proposed by Kozinets has been providing helpful guidelines to researchers (see Quinton & Wilson, 2016). Many researchers, such as Gurrieri and Cherrier (2013), strictly followed the Kozinets guidelines. However, this method has gone through several changes. Valck et al. (2009), for instance, omitted Kozinets’ recommendations regarding the ethical approach, while Bratucu, Gheorghe, Radu, and Purcarea (2014) disregarded the research planning step. A number of other researchers, including Langer and Beckman (2005) and Prior & Miller (2012), even suggested further changes to the procedure.

The ethical issue and triangulation lies at the heart of the discussion on these suggested changes. While Kozinets (2002) emphasized the necessity of having permission to use the data community members provide, other researchers believe that this would make netnography lose its advantage—unobtrusiveness (Di Guardo & Castriotta, 2013; Quinton & Wilson, 2016). These different viewpoints in netnography are known as active and passive approaches. Most researchers prefer the active stance due to its ethical value (Lima, Namaci, & Fabiani, 2014). However, numerous scholars have adopted the passive approach as it provides a naturalist analysis of data, which makes the research unobtrusive and free from researcher bias (Di Guardo & Castriotta, 2013).

Application of netnography

Several academics have developed the literature on netnography, but netnography is still not often used among marketers (Sloan, Bodey, & Gyrd-Jones, 2015). The paucity of its implementation is a result of the anticipated risk often associated with new research methods (Kozinets, 2012). Hence, research providing helpful guidelines to companies on the use of netnography is needed (La Rocca et al., 2014). While netnographic studies could be used in various sectors, researchers stress the great opportunity of netnography in the fashion sector (Parrott et al., 2015) and they recommend further research into its use, to understand its possible influence on this sector. Several research efforts have been conducted focusing on online communities on luxury fashion, and on green fashion communities (see Shen, Zheng, Chow, & Chow, 2014), yet this sector is under-studied by netnographers. Netnography, as a research tool, has much potential in developing countries as there is a high Internet penetration rate (60%–70%), a high public availability of the Internet, and a massive participation of consumers in online communities (especially in fashion-related Internet communities)—particularly among SEE countries (Kelly, Liaplina, Tan, & Winkler, 2017); however, netnography has been rarely used. Zhang and Hitchcock (2017) recommended that a study evidencing its latent quality among these countries would enhance the effectiveness of netnography. As a result, this article decided to focus on the fashion industry in a developing country located in SEE.

Data collection

Considering the requirements of this research, the primary data was gathered through three research modalities as follows.

Sample

Non-probability sampling methodologies were used for this qualitative research targeting SEE consumers and fashion designers, including the following:

Data analysis

The interpretation of the results is performed through thematic analysis. Thematic analysis is considered suitable for researchers who seek to understand the perceptions of participants regarding a specific topic. It offers researchers the ability to identify and create meaningful results, instead of summarizing data (Saunders et al., 2016). This research has followed Clarke and Braun’s (2013) six-phase framework for conducting thematic analysis and found it extremely useful.

Online fashion communities

According to Chan and Li (2010), a shared/common interest is among the key reasons that join people in online communities. The data analysis of the members of “MM” shows that the reasons for consumer participation in online communities do not differ significantly from the views of the modern scholars in the field—in this case, a shared interest in fashion and fashion trends. Leal, Hor-Meyll, and Paula (2014) argue that people join online communities to have ease of access to the information they are seeking without the limitations imposed by geographic boundaries. This preference is noticed among members of “MM” as well; for example, R11 claimed:

We do not need to travel to meet each other and discuss, we go online, and there we meet people with the same interests—the power of Internet.

The majority of participants describe their activities in the group similarly to Liou et al. (2016), who defined a virtual community as an “online social network, constructed on the basis of social interactions for sharing information and knowledge” (p. 188). Leal et al. (2014) argued that sharing information and experiences among members is more difficult to evaluate when interactions occur online because members trust each other less in such forums. Interestingly, only participant R9 had a view similar to these scholars:

A lot of people express themselves differently online, compared to how they are in real life.

While the majority of members stated that they consider the interactions within that community to be honest and straightforward, it is believed this is a result of the anonymous identity which encourages them to share their opinions and experiences.

The members of such communities have a considerable impact on businesses, because they recommend, promote, or criticize brands and share their experiences with each other (Mahajan, 2015). Evidently, their impact on brand perceptions is recognized by both marketing researchers and members of the online community. R6 is among them:

Businesses have profited a lot, mainly because a member posted about buying an item there, and that is what particular businesses previously needed to be more frequented.

R4, on the other hand, noted that members’ impact is the greatest on brand promotion, especially the recently established ones:

. . . there are designers who are not famous, but they post in the group and immediately become appreciated.

R4’s comment demonstrates that online member groups are being used by fashion marketers to promote their brands and that designers are seen as group members, contributors, as well as sellers/marketers of fashion.

User participation motives

Marketing researchers benefit from the fact that consumers share all the relevant data for market research freely within online communities as this suits their goal of gaining valuable consumer insights and understanding consumer perceptions and preferences (Dessart et al., 2015). As a result, researchers are interested in the motives that drive members to participate in them. Zha et al. (2014) considered that people engage in an online community primarily to share information. Dessart et al. (2015) and Teichmann, Stokburger-Sauer, Plank, and Strobl (2015) noted a number of other motives, such as information acquisition, fun and enjoyment, reputation, or relationship building. The analysis of focus groups for this research presents similar results; members of “MM” have been motivated to engage in the community for various reasons, as given in Table 1.

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Table 1.

User participation motives.

Active community

Immediate response

Biggest fashion community

Easier access

Fashion enthusiasts (common interest)

Congenial information environment

Friends’ suggestions

Information exchanges

Anticipated reciprocity

Reputation

Sense of efficiency

Sense of belonging

Altruism

Almost all the participants at one point during the interviews reiterated the importance of information sharing and its value in the community. R9 said:

Members in the group share almost everything . . .

Meanwhile, the netnographic study conducted within this community noted the willingness of people to share information, either based on their experience, or that of their friends, what they heard from advertising, marketing, or sales people.

Dessart et al. (2015) claim that an active community and immediate response are the other motives members interact in online communities. Similarly, participants claimed that they engage in “MM” because the community is very active, and members respond to their queries straight away. R12 said that:

“followers there are really active . . ,” while R6 noted, “ . . Whenever one posts, they get an immediate response.”

The netnographic analysis showed similar results—80% of members post approximately 200 messages per day, and during the research, the immediacy of members’ responses was noted and recorded in field notes.

Members participate in “MM” for a number of other deeper motives, such as avoiding fashion mistakes, fulfilling ego, being “trendy,” seeking product information from firsthand experience, out of curiosity, or even due to group diversity (Table 2).

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Table 2.

Motives to engage online.

R9: “I have always needed to follow posts of people about fashion, so that I am able to know more about it.”	Avoiding social-fashion mistakes
R13: “I want to be inspired by different trends in our country and abroad.”	Being “trendy”
R12: “I joined MM to see what is going on, finding out what members post each day, and to see what trendy people are wearing . . .”	Curiosity

The concept of netnography

Netnography, as a qualitative marketing research method, is considered to be relatively new for fashion practitioners (La Rocca et al., 2014). Although Kozinets (2012) stated that this is a result of the fear of adopting new research methods, this research shows that, at least among emerging countries, the neglect of such a method is the consequence of a lack of knowledge and ability to implement. Only a few fashion designers are aware of netnography, and a few actually conduct market research.

As for members of online fashion communities, none of them have heard of netnography before, and many of them had never considered that their interactions in the community could be analyzed by marketers. As R6 said:

I have no idea what netnography is . . . I never gave it a thought that there are people on that professional scale that might look deeply into our posts.

Although unaware of netnography as a method, a few participants were aware that their social interactions in such communities are observed by marketing researchers. R12 noted:

People’s posts, comments and likes are usually observed, that is why I am careful in what I show the world.

On the other hand, fashion designers were more informed about netnography. P4, who is familiar with the method, views it as a marketing tool that “provides an in-depth understanding of the consumer perceptions on online sites.” His perception is similar to Bartl et al.’s (2016) netnography definition—a “method that explores digital tribes and consumer behaviour” (p. 165). Besides P4, P6, who is the only fashion designer, whose team uses this method, gave a detailed explanation of the method:

I am familiar with the term; actually, it is one of the methods used by my brand . . . Netnography, or as I refer to it—online ethnography, is an online method . . . and it studies the online social interactions.

His perception of netnography is similar to Hine’s (2000) definition. Nevertheless, for the majority of fashion designers, netnography was a new term, and they had heard it for the first time when they were asked to participate in the interview.

Perceptions about netnography

Fashion designers’ perceptions regarding netnography as a research method are mostly positive, and resemble the claims of scholars. Prior & Miller (2012) and La Rocca et al. (2014) considered netnography as an effective tool for gaining access to, and understanding consumer perceptions, needs, and experiences about products and brands. P1 had a similar opinion. She stated:

. . . nowadays, people tend to express themselves freely on social media. This offers us—the brands—the opportunity to understand their preferences, opinions on a garment, a new style, or trend, their favourite colours during a season.

In addition, P6 emphasized the following:

It offers the possibility to obtain information about social interactions naturally,

which is, according to Kozinets (2012) and Pollok et al. (2014), the most significant strength of this method. P6 noted some of the other advantages that this method provides marketers, such as:

. . . it offers relevant and detailed data. The brand learns consumer motives and reasons behind their brand loyalty.

The relevance and quality of data are mentioned also by Xun and Reynolds (2010) and Prior & Miller (2012), whereas Healy and McDonagh (2013) noted the importance of the method in understanding consumer loyalty.

P1 recognized a characteristic of the method, which has not been discussed by scholars:

It provides us with other information, such as their opinion on prices, fabrics and on competitive brands.

Although scholars have not discussed it, it is clear that these data can offer a great amount of insight about competitors; this is also noted during the netnographic research conducted in “MM.”

In addition, Pollok et al. (2014) considered the method to be significant in creating innovative designs. The fashion designers, however, did not agree with such a statement. While they recognized its significance in the process of creating new trends, they did not deem it as essential to the process.

Similar to fashion designers, the majority of members of “MM” had mostly positive views about netnography as a research method. R11 stated:

I find it very positive to have my posts observed, that means the observers are interested in my posts.

Similarly, R10 said,

I would be happy to have my posts read because it may help a company advance.

These views were not expected by the researcher, because Kamboj and Rahman (2017) mentioned the unwillingness of members of online communities to participate in such observations. Among participants, only R4 and R5 were against such a method, and showed reluctance to participate.

R4 said, “I would not post, if I knew I was being observed”; whereas R5 claimed: “I am an introverted person . . . I would not post because I appreciate my privacy.”

Besides the unwillingness to participate in such research, the above-mentioned responses show that some members have the tendency to self-censor their comments when they become aware of external (non-community) observers, or because of shyness (Hayes, 2007). Others tend to self-censor their comments out of fear of social isolation, or lack the required knowledge to participate in the conversation.

R8: If I knew I was being observed, I would be very careful on the language I would use and the thoughts I choose to express.

Perceptions on netnography’s challenges

There are a number of challenges that researchers face while conducting the method. Prior & Miller (2012) and Clemente-Ricolfe (2017) stressed three crucial challenges: “community scope, data validity and data reliability.” The data analysis shows that fashion designers’ perceptions about these challenges bear some similarities with those of scholars in the field. P1, for instance, considered that:

the main challenge is a large amount of data; it is too hard to go through everything consumers post—it takes time.

At the same time, the researcher noticed the issue with the scope of the data when analyzing the insights collected from “MM”; the researcher had to deal with a large volume of data. Therefore, future researchers, similar to the researcher, have to understand data complexity and manage their research efficiently by creating a platform.

Besides, the difficulty in establishing the credibility of the data is noticed by both the fashion designers and the researcher. In this regard, the possibility of creating false and anonymous identities is considered a major challenge as Prior & Miller (2012) mentioned.

Netnography’s procedures and experiences

Both fashion designers’ and researcher’s perspectives have been considered to understand the experiences of fashion designers on conducting netnography. Out of seven of the respondents only one fashion designer—P6, utilized and was familiar in detail with netnography. A few of them had never conducted this type of research, and a small number of them followed a similar research method, which they have not yet labeled. The researcher has followed Kozinets’ (2002) established procedures, with a few divergences.

The analysis shows that P6 does not follow strictly the established rules by Kozinets, yet, he considers the method to be netnography, unlike P1 and P2, whose research methods resemble netnography, but are not categorized as such. P6 has a marketing research team, which collects and analyzes data. The P6 team also conducts a few other research methods; thus, it is not solely focused on netnography and does not have trained experts in the method. This fashion designer regards the team as sufficient for the process, although Pajo, Vandevenne, and Duflou (2017) noted that a team would require trained experts to perform a successful analysis. Like P6, the researcher conducting the netnographic study of “MM” does not have a team of trained experts—in fact, the research is conducted only by one person, who is familiar with netnographic procedures through scholarly studies. The analysis shows that both have succeeded in conducting such a method, therefore, it is believed that Pajo et al.’s (2017) suggestion of a trained team of experts, is not crucial to the success of the research. Having experience with netnographic research over time can only enhance a fashion designer’s product offering, branding, and overall marketing strategy.

P6 emphasized the importance of team collaboration:

It is vital for the team to cooperate because sometimes the team gets inundated with data.

He briefly described the process:

The team considers various online sites, but mostly focuses on brand’s Facebook and Instagram pages. Only the data relevant to the research is collected. The selection of data is among the first steps. Then follows its analysis, our team uses a data analysis tool that has been purchased for this purpose. Whenever useful feedback is noted, the team records it, and informs the fashion forecasting team. The process takes a lot of time, but it is very useful for the brand.

This process does not resemble Kozinets’ (2002) five guidelines; P6, like Valck et al. (2009) and Bratucu et al. (2014), changed the procedure. Similar to Valck et al. (2009), P6 omitted the ethical approach aspects suggested by Kozinets (2002), because, like many scholars, P6 assumed a passive stance. However, P6 did not omit all of the Kozinets (2002) guidelines; P6 followed Kozinets’ (2002) suggestion on simplifying the process by categorizing the data into relevant and irrelevant, and analyzing only the relevant ones. In general, P6 seemed to follow a more traditional approach of data collection; he focused mostly on the brand’s online communities. Although this is acceptable and many practitioners have implemented it, Malinen (2015) argued that this enables “passive consumption of information by consumers”; thus, online communities created by consumers are more desired.

On the other hand, the research of “MM” is more similar to Kozinets’ (2002) studies. The research went through all of his suggested steps. It gave special attention to the first step—research planning because it helped the researcher set the primary goals and prepare better for the research. Although this step has been omitted by certain scholars (Bratucu et al., 2014), the analysis shows that it is important to the process. The research evaluated potential online communities using Kozinets’ (2002) five criteria; this step aided in identifying the most appropriate online fashion community—“MM.” Afterward, the researcher spent a considerable amount of time studying the community and getting familiarized with it. It was only then that the process of data collection started. Similar to Kozinets (2002) and P6, the researchers classified the data as on-topic and off-topic. In addition to this, the 80/20 active rule was noticed—80% of posts are from 20% of members. Consequently, the research identified four types of members: Tourists (36%), Minglers (38%), Devotees (19%), and Insiders (7%).

As for data analysis, P6 and the researcher followed different approaches. P6 stated that his team used a data analysis tool—a software, whereas the researcher conducted a manual analysis through thematic analysis. Kozinets (2002) stated that this process can be done by utilizing both of them. However, manual analysis is deemed too time-consuming. While P6 provided no information on the method they used to ensure data validity, the research followed the suggestions of Prior & Miller (2012), and has thus tried to ensure its validity by triangulating data with the aid of two focus groups composed of the members of “MM.”

While P6 conducts ongoing research, the researcher investigated “MM” for only 3 months due to time limitations. Scholars like Valck et al. (2009) preferred a longer period of studies. Nevertheless, time does not seem to be a determining factor for this research, since Kozinets himself conducted 2 to 3-month long studies. In view of that, if the researcher intends to investigate, for instance, a major fashion event and get an initial reaction to trending comments—even a shorter study (a day, or a week study) might be appropriate and useful. As for the number of communities involved, Pollok et al. (2014) suggested simultaneously analyzing several online communities, unless research purposes can be achieved through using only one community. Thus, the research focused on only one community, the most relevant.

Perceptions regarding the active–passive stance

Although scholars generally prefer taking the active approach, due to its ethical value (Lima et al., 2014), some take the passive stance (see Di Guardo & Castriotta, 2013). As it was mentioned previously, P6 took a passive stance. P6 preferred this approach since:

it provides a naturalist analysis of data, because, when informed participants might decide to cease commenting, or be more careful with their posts, it might create fake data.

His view resembles Di Guardo and Castriotta’s (2013), who claimed that a passive approach provides a naturalist analysis of data, but pushes participants away, and therefore, it limits the research from gaining in-depth information.

On the other hand, P5 agreed with Lima et al. (2014), who favored the active stance due to ethical issues:

P5: An active stance is more ethical, and I believe all participants should be informed, otherwise, their privacy is invaded.

Until now, studies were concerned with the opinion of researchers on the subject, and neglected online community members’ views about the stances. This analysis, however, shows the perceptions of online fashion communities’ members regarding the approach that the researcher should follow when conducting netnography. Members of “MM” were asked whether they would prefer to be informed about the research prior to its implementation, and these were their responses:

R6: If you are going to analyse my posts, at least inform me about it.

R8: It is everyone’s right to be asked for permission.

Their views are similar to Lima et al.’s (2014) claim that participants would want to be informed in advance. However, R4 disagreed with these views; they stated, “We would not be so authentic anymore,” which led to a discussion in both focus groups about the impact of the active approach on members’ activities.

While three members stated that the observation would not impact them at all, the rest of them admitted that their consciousness of them being observed would impact them, either entirely or partially—they would stop posting, change and consider the way they write, or even leave the community.